Gastos Casa
25 August, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 29/3/2025 | Comida | 70591 | Tami | Supermercado |
| 3/4/2025 | Gas | 83300 | Andrés | NA |
| 4/4/2025 | Agua | 20807 | Andrés | NA |
| 6/4/2025 | Comida | 52655 | Tami | Supermercado |
| 12/4/2025 | Comida | 72108 | Tami | Supermercado |
| 16/4/2025 | VTR | 21990 | Andrés | NA |
| 22/4/2025 | Comida | 107881 | Tami | Supermercado |
| 26/4/2025 | Comida | 55874 | Tami | Supermercado |
| 28/4/2025 | Comida | 13050 | Tami | Cervezas MUT |
| 29/4/2025 | Electricidad | 52507 | Andrés | enel |
| 29/4/2025 | Diosi | 11990 | Andrés | arena 7kg superzoo |
| 3/5/2025 | Agua | 17072 | Andrés | aguas andina |
| 13/5/2025 | VTR | 22000 | Andrés | NA |
| 17/5/2025 | Electricidad | 52404 | Andrés | NA |
| 13/6/2025 | VTR | 22000 | Andrés | NA |
| 22/6/2025 | Electricidad | 52401 | Andrés | NA |
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 10/8/2025 | Comida | 108649 | Tami | Supermercado |
| 12/8/2025 | Enceres | 13490 | Tami | Confort |
| 16/8/2025 | VTR | 22000 | Andrés | NA |
| 17/8/2025 | Comida | 72586 | Tami | Supermercado |
| 20/8/2025 | Electricidad | 65242 | Andrés | 49393- 42306 -47872= 1521 |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0200e+09 2 5.2713 0.0053 **
## lag_depvar 2.6335e+11 1 2721.9292 <2e-16 ***
## Residuals 8.2142e+10 849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1766.028 16223.70 0.1430829
## 2-0 31328.843 23238.654 39419.03 0.0000000
## 2-1 24100.005 19421.810 28778.20 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
## 854 55308.00 2 49436.29
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 697 53563.10 21907.996
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29 55308.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2014.442788 4038.467928 -536.146901 2439.740343 -2965.825240
## 7 8 9 10 11
## 520.129605 -5653.814103 -1189.994362 -3968.548863 -422.697055
## 12 13 14 15 16
## -4943.757909 -1616.292282 -906.573975 371.262926 -3247.568905
## 17 18 19 20 21
## -384.040300 -2135.310210 6598.400141 -1528.908789 -1208.827462
## 22 23 24 25 26
## 1474.613892 -1185.976257 234.684364 1695.619965 -7099.798036
## 27 28 29 30 31
## 944.635627 8191.100197 423.877766 -8.175543 -2394.975463
## 32 33 34 35 36
## 1579.802704 4577.385388 1135.217010 2400.116865 -1858.035809
## 37 38 39 40 41
## 4615.769955 4308.382896 -2268.022139 -2976.382405 -1107.936158
## 42 43 44 45 46
## -10740.314511 7282.218352 2556.571581 1368.222286 8107.445860
## 47 48 49 50 51
## 694.356055 6536.507974 6726.190729 -5866.855458 -4786.309432
## 52 53 54 55 56
## -5055.324424 -7928.495356 6123.913634 -4077.081264 -4897.795968
## 57 58 59 60 61
## 3849.088689 884.898534 -33.989940 140.583923 -4997.735631
## 62 63 64 65 66
## 18121.882744 3650.161482 -3634.975143 5932.428264 7354.836219
## 67 68 69 70 71
## 14654.051783 1717.765402 -13189.764204 -1295.843032 4651.702430
## 72 73 74 75 76
## -4889.472952 -4398.606589 -10495.380003 2461.154236 -5402.640169
## 77 78 79 80 81
## 1057.507946 -6870.745542 538.182243 -2361.617562 -2701.548032
## 82 83 84 85 86
## -3940.356843 -547.588038 2305.598787 3756.580583 474.321478
## 87 88 89 90 91
## -486.456482 194.597744 4300.330530 -1161.296520 1151.537711
## 92 93 94 95 96
## -2063.005357 -1044.460599 176.729480 274.207888 -7484.301624
## 97 98 99 100 101
## 2386.298190 -8605.436999 -2949.007695 -4048.600024 -1747.213924
## 102 103 104 105 106
## -1271.384096 3171.587603 -2347.837919 2587.456609 -1162.218691
## 107 108 109 110 111
## 966.414281 2584.323699 -3154.926190 -4725.647538 -855.713786
## 112 113 114 115 116
## 1898.295314 11690.419647 -1237.065245 2672.562079 4268.339926
## 117 118 119 120 121
## 3510.604119 -1090.414302 -4708.632890 -3720.527683 2320.434283
## 122 123 124 125 126
## -1730.290540 1341.418890 8860.200244 855.175865 138.238361
## 127 128 129 130 131
## -2514.229116 2659.581548 7058.469258 1022.732473 -8489.572835
## 132 133 134 135 136
## 1751.919030 4139.220514 -3157.706521 -1416.397648 -851.955045
## 137 138 139 140 141
## -3878.728692 1181.558932 -495.832355 -2914.154291 1715.747511
## 142 143 144 145 146
## -1881.889916 -7831.234922 2032.382862 -3484.390136 2095.763118
## 147 148 149 150 151
## -261.625924 1019.243689 -361.872447 1349.719562 1185.438404
## 152 153 154 155 156
## 3356.389402 -4859.576499 -1175.882298 -3237.805027 5952.776374
## 157 158 159 160 161
## 9747.403320 -3660.466534 -5008.022838 3372.484374 -32.679997
## 162 163 164 165 166
## 2469.790973 -6134.573343 -6974.924075 3925.808886 17165.042193
## 167 168 169 170 171
## 3403.871021 -622.501482 -2671.161300 -1330.986360 3362.625128
## 172 173 174 175 176
## -455.646116 -8303.694932 2632.201790 4094.496843 395.306634
## 177 178 179 180 181
## 8519.706690 -9477.590937 -3704.261347 -10978.516671 -11477.253598
## 182 183 184 185 186
## 994.864804 9054.201125 -1666.562395 5691.416459 6318.277980
## 187 188 189 190 191
## 12919.955924 8190.186570 -4307.693055 2215.661622 10115.903304
## 192 193 194 195 196
## -1899.896483 -2703.807674 -10541.891913 -6625.367837 971.715641
## 197 198 199 200 201
## -5494.672084 -10056.314437 5125.242979 -3325.191512 -1967.971228
## 202 203 204 205 206
## -1058.561351 6240.638609 9625.988537 317.551199 2662.175053
## 207 208 209 210 211
## 2833.725926 5518.680435 12566.113553 -5958.319534 -11565.681070
## 212 213 214 215 216
## -5932.520022 -10850.947167 -5334.704884 1270.221198 -13265.906940
## 217 218 219 220 221
## 16138.535985 7554.448227 1270.031885 26428.759206 12255.308480
## 222 223 224 225 226
## 7057.484923 13745.337118 -4200.634268 -2026.562106 3493.202505
## 227 228 229 230 231
## 76.187073 2464.551689 8725.176389 5552.432265 -2180.960874
## 232 233 234 235 236
## -2099.282477 9157.049352 -11778.114029 -7549.122877 -8804.210003
## 237 238 239 240 241
## -10361.626524 2820.845292 1095.488296 -8553.227551 -9242.599363
## 242 243 244 245 246
## 8844.990365 -8017.634353 2237.652222 -10551.636406 -4301.122388
## 247 248 249 250 251
## 1176.601698 755.864554 -12564.214580 3398.790201 1815.512585
## 252 253 254 255 256
## 3962.717935 1882.468320 -1415.233047 10883.628820 20617.687086
## 257 258 259 260 261
## 2918.236994 -4554.343393 3827.659157 -1979.514291 3453.078620
## 262 263 264 265 266
## -5137.993799 -11176.627201 -5004.625441 -794.417069 -5460.426396
## 267 268 269 270 271
## 8509.025844 -4556.632120 3915.127966 -2385.362893 4153.045198
## 272 273 274 275 276
## 426.739043 7019.167880 -1703.151583 11734.190730 -4887.972478
## 277 278 279 280 281
## 1424.382165 -675.393664 7548.644544 -5367.985692 -3035.285343
## 282 283 284 285 286
## -11560.714950 -2953.949210 18374.585428 7482.077581 2423.681495
## 287 288 289 290 291
## -941.696781 594.603419 6086.745638 6564.080955 -19097.762983
## 292 293 294 295 296
## -11434.978299 -8398.071798 9402.809483 2797.983643 -1456.383901
## 297 298 299 300 301
## 27127.333926 9749.884846 4571.962512 9184.663063 2512.541208
## 302 303 304 305 306
## -1375.997568 7559.651611 -24639.316370 -3831.601118 -461.295631
## 307 308 309 310 311
## -7249.812782 -4237.732832 2676.186550 -9450.949099 -3469.987340
## 312 313 314 315 316
## -8418.335150 1349.509946 -3370.160592 1834.060788 -4302.650662
## 317 318 319 320 321
## 27230.273873 -1013.613313 3002.642820 10535.634520 5274.618449
## 322 323 324 325 326
## 32058.269471 4732.988311 -21313.536392 1467.309183 788.393806
## 327 328 329 330 331
## -6782.942948 -2033.214443 -33558.109581 698.885618 -2483.584875
## 332 333 334 335 336
## -267.165666 -3340.446769 3920.085403 -612.599377 -7127.923213
## 337 338 339 340 341
## -3277.284138 -2347.115908 -7832.287138 3713.578452 -1522.834438
## 342 343 344 345 346
## -1890.035963 -1145.778022 23.303347 324.022788 -1781.304413
## 347 348 349 350 351
## -9609.665393 -13355.422626 2190.120464 -4455.624680 -3786.510007
## 352 353 354 355 356
## -6105.347101 1633.117154 1254.371955 2611.742945 -3922.702098
## 357 358 359 360 361
## -669.922465 518.440687 6846.801073 85.654011 -233.792616
## 362 363 364 365 366
## 2384.359817 -2958.395742 -1078.970513 -8943.230888 -4801.210265
## 367 368 369 370 371
## -6375.854252 -5097.642493 -7390.325768 4892.716346 226.550397
## 372 373 374 375 376
## 6967.769144 -7814.723359 -2422.303028 -3544.182709 -2617.092779
## 377 378 379 380 381
## -12604.393607 1786.979311 -10762.923453 5590.998101 9204.713017
## 382 383 384 385 386
## 2958.484634 -2581.715310 1424.033321 6553.932001 11195.517683
## 387 388 389 390 391
## -6056.552221 -5603.873176 -385.463300 8333.554404 1556.855593
## 392 393 394 395 396
## 10957.314145 -10178.405311 2504.139238 434.472130 283.284251
## 397 398 399 400 401
## -932.980004 -838.356753 -14758.833172 8304.469088 -1421.162813
## 402 403 404 405 406
## -1605.513144 6755.740807 -8179.494114 -1514.268331 -2741.336403
## 407 408 409 410 411
## -6017.418427 -3035.381834 -4083.100903 -8908.789643 6006.700240
## 412 413 414 415 416
## 1487.805352 -7536.086626 -7833.894164 14100.148405 3637.263472
## 417 418 419 420 421
## 4292.509875 -8255.970880 -4937.833094 -2778.660713 2650.426873
## 422 423 424 425 426
## -14191.852250 -2926.639021 -9228.405947 2909.137986 6855.318743
## 427 428 429 430 431
## 6421.787128 -4169.845449 -4292.587451 -4882.706318 -1937.386986
## 432 433 434 435 436
## -5857.772776 -6758.014735 -6064.708455 -1495.807359 -953.990118
## 437 438 439 440 441
## -5086.757671 2478.371230 4718.568984 -5201.805356 -2292.307969
## 442 443 444 445 446
## 1443.267532 -3981.700380 2698.349518 -6730.518138 -12245.863104
## 447 448 449 450 451
## -4613.329534 9549.930323 -2166.090744 4621.645901 -6022.539946
## 452 453 454 455 456
## -1260.485683 245.166884 2882.009219 -12426.269304 3246.033884
## 457 458 459 460 461
## -6840.358536 6399.197463 2863.482200 2345.130263 -4018.175986
## 462 463 464 465 466
## 1930.432499 -178.517340 1620.045536 -701.301487 3170.856940
## 467 468 469 470 471
## -2830.068508 5622.948005 -7141.827154 -3139.971482 -2370.608713
## 472 473 474 475 476
## -4821.859199 2852.181436 7641.842479 -6197.941717 1322.306715
## 477 478 479 480 481
## -6345.831904 -2992.868620 1870.502245 -13079.663967 -9869.628296
## 482 483 484 485 486
## -1294.242300 -75.537901 -1065.214474 -1448.762884 -9694.164330
## 487 488 489 490 491
## 11006.590262 6106.886497 7269.143016 -5612.177343 5206.689268
## 492 493 494 495 496
## 9114.940171 5848.186783 -13695.121349 -10744.411204 -3587.757951
## 497 498 499 500 501
## -1243.674141 -661.007626 -7763.360084 493.571791 4164.926871
## 502 503 504 505 506
## 5370.231315 503.754481 -79.007169 -7399.516947 429.549071
## 507 508 509 510 511
## -5192.218095 1701.207782 -1435.644660 -8295.638877 -719.560620
## 512 513 514 515 516
## -2794.388803 -704.216694 1212.971241 -9622.364140 -7870.063409
## 517 518 519 520 521
## 24197.019262 9661.085567 5686.173593 -5544.586018 2606.582156
## 522 523 524 525 526
## 16820.456761 11228.206666 -24421.849228 -5256.864538 -3913.671177
## 527 528 529 530 531
## 4403.396563 -538.607036 -11282.893916 4243.349263 13747.516474
## 532 533 534 535 536
## -5179.279501 4186.762378 5355.912319 -2004.066275 -4746.809404
## 537 538 539 540 541
## -7264.266826 -2268.435665 8161.304697 -57.664572 -8325.349692
## 542 543 544 545 546
## 1656.556907 -764.415382 203.118732 -11195.230923 -11195.083568
## 547 548 549 550 551
## 1935.606529 6887.629276 -1457.414393 698.376620 -7864.095591
## 552 553 554 555 556
## 8440.996722 755.025587 -12100.285711 9037.793290 8502.665739
## 557 558 559 560 561
## -83.250839 4670.065804 -3771.438918 13922.382447 21272.398661
## 562 563 564 565 566
## -6751.538605 -9939.585620 6550.191579 -13.974716 3219.128757
## 567 568 569 570 571
## -7617.346630 -17520.394485 6505.854579 6260.707109 1712.537029
## 572 573 574 575 576
## 2906.356808 1572.580545 -2363.710070 14527.993025 -9877.523991
## 577 578 579 580 581
## -6441.951438 8533.910673 2657.890633 -6751.055982 7325.153914
## 582 583 584 585 586
## -4002.516686 -2964.163994 15524.361239 -14719.762577 8251.757112
## 587 588 589 590 591
## -127.786286 -6407.204651 -928.022738 82.467604 -10821.575511
## 592 593 594 595 596
## 1659.592441 -7287.259542 2943.994128 8732.200961 -7660.777263
## 597 598 599 600 601
## 5711.751624 2577.731225 6690.861641 -3373.321420 5977.152969
## 602 603 604 605 606
## -8485.995566 2093.203867 1102.877741 2969.225161 1319.382858
## 607 608 609 610 611
## 219.915775 -5986.510559 7916.946154 -1361.800642 -2745.890374
## 612 613 614 615 616
## -3615.286437 -8378.782226 11831.107268 4776.882893 -9485.260390
## 617 618 619 620 621
## 11482.384753 5876.448892 -5758.905158 26192.950479 -13053.846308
## 622 623 624 625 626
## -6965.294190 2994.272343 -4326.911718 -10746.203710 11170.709264
## 627 628 629 630 631
## -21789.295282 -2515.664390 8577.317657 11013.981085 -1701.672483
## 632 633 634 635 636
## 33140.132737 -6808.235366 5519.728606 5194.420715 -2479.022046
## 637 638 639 640 641
## -5542.676200 -2118.601668 -12600.378356 -2379.513516 -2017.540697
## 642 643 644 645 646
## -2647.127454 -2979.317806 1699.458388 4309.738434 16832.484567
## 647 648 649 650 651
## 18381.510527 671.848932 4581.545520 10399.544765 19921.747636
## 652 653 654 655 656
## 484.207664 -28310.036981 -1512.878520 -2452.468315 1718.682590
## 657 658 659 660 661
## -3339.571044 -10757.285572 1554.313631 4113.958146 -1126.250278
## 662 663 664 665 666
## 12916.262548 1220.631751 1674.294614 -11830.504933 1264.198994
## 667 668 669 670 671
## 1071.259838 -5282.509605 -7515.333047 1975.855179 -3806.140472
## 672 673 674 675 676
## 2585.196268 -3472.585475 -9425.218544 -8381.797423 -3045.510600
## 677 678 679 680 681
## 103.841121 2773.170737 631.272911 -3912.226807 -1890.812192
## 682 683 684 685 686
## -1400.671662 -8325.625247 4574.386601 -2326.897891 -1482.043307
## 687 688 689 690 691
## 503.142579 10766.156535 9745.455832 10506.847966 -9790.182213
## 692 693 694 695 696
## -3662.810763 -3241.591387 5774.385219 -10488.917071 -7999.863868
## 697 698 699 700 701
## -8690.017558 -6344.743946 -4805.621980 3017.147073 -4474.228557
## 702 703 704 705 706
## -1968.751074 4150.531751 31027.939447 9433.811825 23365.138298
## 707 708 709 710 711
## 1614.082372 8263.692777 22869.402646 6525.278148 -18230.276373
## 712 713 714 715 716
## 4793.111891 -5469.151408 -128.790955 450.628753 -17296.588793
## 717 718 719 720 721
## -5303.306228 3293.249930 -3053.198997 -13019.369990 4233.196249
## 722 723 724 725 726
## -5599.992684 696.656464 -3979.501604 -12493.296452 1316.693163
## 727 728 729 730 731
## -1916.134357 -9826.454562 17216.611156 1731.846290 -2765.092069
## 732 733 734 735 736
## 5671.886533 -8670.973005 -767.559257 8094.322946 -15391.444285
## 737 738 739 740 741
## -5957.771666 7359.469032 -4829.079652 114.537967 1782.088098
## 742 743 744 745 746
## -2000.164718 -5212.841374 6366.004064 -6318.098314 22653.102771
## 747 748 749 750 751
## 7792.893562 -1977.869552 -7320.875633 23379.675310 -4315.569049
## 752 753 754 755 756
## 1368.991949 -14449.427474 56073.321869 26921.808847 15114.126560
## 757 758 759 760 761
## -10618.868239 10623.943480 7335.665305 5831.923242 -46366.626045
## 762 763 764 765 766
## -16183.879114 941.698115 -2540.832695 -3482.338686 122817.747609
## 767 768 769 770 771
## 19389.205956 43805.398506 22698.457797 12194.795193 15961.214290
## 772 773 774 775 776
## 25840.328008 -98688.292172 -6739.476052 -35823.724257 1711.047430
## 777 778 779 780 781
## -1251.726712 3373.393927 -7433.105802 -1468.988447 -1968.936124
## 782 783 784 785 786
## 3400.632694 -7174.224820 -2260.943587 3895.113478 2246.509056
## 787 788 789 790 791
## -2774.419813 -4064.104708 1719.868883 2837.532868 -63.454776
## 792 793 794 795 796
## -6714.644487 -5799.051634 -1167.841869 -1283.017506 -7836.733713
## 797 798 799 800 801
## -2378.947815 -3293.216653 -2691.439662 10698.251399 2227.081551
## 802 803 804 805 806
## 7068.510643 2919.286692 -5449.920788 8180.663229 9875.419053
## 807 808 809 810 811
## -10593.208073 -7398.100737 -7513.396881 2991.765334 4184.042422
## 812 813 814 815 816
## -2275.325136 -14172.316960 -4129.580428 6238.861257 8223.859504
## 817 818 819 820 821
## -9678.071092 -7762.780100 -9354.266709 9729.810154 -1236.372181
## 822 823 824 825 826
## -4385.020178 -8462.657534 7853.377890 7901.450736 4970.534441
## 827 828 829 830 831
## -3103.162229 -715.662531 2887.560067 4666.377471 1625.543002
## 832 833 834 835 836
## -6692.340114 2084.202722 -401.394235 2749.592837 4138.720484
## 837 838 839 840 841
## -1135.687955 -9335.767476 5667.864143 -4864.825858 -7584.884227
## 842 843 844 845 846
## 3009.887084 -13052.331023 -2836.633882 11610.406623 1303.878761
## 847 848 849 850 851
## 629.379128 -666.490232 4507.366345 12684.302964 -3682.043091
## 852 853 854
## -11564.003784 -1218.227871 5328.768512
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17254.84 20100.53 24352.29 24070.40 26422.54 23756.58 24472.53 19707.14
## 10 11 12 13 14 15 16 17
## 19443.83 16787.98 17565.04 14296.15 14347.29 15011.59 16707.28 15028.18
## 18 19 20 21 22 23 24 25
## 16062.31 15436.17 22514.91 21599.40 21079.53 22968.55 22294.89 22947.09
## 26 27 28 29 30 31 32 33
## 24792.08 18723.65 20448.90 28282.12 28339.75 28012.83 25643.48 27045.19
## 34 35 36 37 38 39 40 41
## 30886.21 31234.45 32642.89 30154.80 34134.62 37341.02 34398.67 31211.22
## 42 43 44 45 46 47 48 49
## 30059.60 20644.07 28158.86 30594.06 31682.70 38517.22 38012.06 42671.81
## 50 51 52 53 54 55 56 57
## 46905.86 39607.60 34178.90 29204.21 22352.23 28638.94 25221.37 21520.91
## 58 59 60 61 62 63 64 65
## 25926.96 27185.85 27482.70 27894.31 23767.40 40349.98 42192.98 37441.43
## 66 67 68 69 70 71 72 73
## 41646.16 46559.23 57221.81 55236.62 40487.56 37994.73 41011.04 35314.18
## 74 75 76 77 78 79 80 81
## 30768.81 21477.13 24676.93 20604.78 22689.75 17587.96 19602.33 18829.26
## 82 83 84 85 86 87 88 89
## 17857.50 15927.45 17204.54 20810.71 25226.11 26215.46 26240.40 26856.81
## 90 91 92 93 94 95 96 97
## 30979.73 29810.89 30809.72 28875.17 28075.41 28443.36 28849.73 22430.56
## 98 99 100 101 102 103 104 105
## 25444.01 18478.15 17334.89 15376.64 15676.24 16353.27 20823.55 19907.54
## 106 107 108 109 110 111 112 113
## 23416.79 23206.87 24882.10 27757.35 25256.79 21702.14 21977.42 24622.29
## 114 115 116 117 118 119 120 121
## 35481.07 33674.87 35511.37 38508.11 40462.99 38152.63 32976.38 29319.71
## 122 123 124 125 126 127 128 129
## 31401.43 29682.30 30863.23 38458.97 38101.62 37163.66 34028.85 35809.10
## 130 131 132 133 134 135 136 137
## 41204.12 40644.72 31851.08 33115.21 36303.28 32715.83 31103.96 30189.44
## 138 139 140 141 142 143 144 145
## 26748.30 28161.98 27931.73 25619.25 27642.60 26268.09 19873.62 22902.53
## 146 147 148 149 150 151 152 153
## 20730.38 23705.91 24245.61 25835.16 26017.14 27670.42 28970.47 32001.01
## 154 155 156 157 158 159 160 161
## 27473.60 26736.95 24293.51 30184.45 41680.90 40012.02 37378.37 42395.97
## 162 163 164 165 166 167 168 169
## 43803.78 47217.86 42686.21 37995.91 43418.24 59711.70 61922.64 60337.59
## 170 171 172 173 174 175 176 177
## 57164.99 55565.09 58266.22 57290.84 49587.08 52409.07 56149.69 56185.86
## 178 179 180 181 182 183 184 185
## 63310.88 53818.26 50570.95 41384.54 32928.42 36434.80 46532.85 45989.15
## 186 187 188 189 190 191 192 193
## 51938.72 57680.62 68457.81 73737.84 67435.91 67629.24 74695.75 70374.52
## 194 195 196 197 198 199 200 201
## 65899.75 55149.37 49182.71 50606.24 46203.31 38376.33 44797.62 43025.97
## 202 203 204 205 206 207 208 209
## 42664.13 43142.22 49932.58 58817.02 58446.82 60170.70 61825.61 65614.74
## 210 211 212 213 214 215 216 217
## 75076.18 67163.25 55358.66 49970.38 40971.56 37930.92 41042.91 31068.46
## 218 219 220 221 222 223 224 225
## 48032.84 55349.68 56251.10 79004.26 86495.23 88497.38 96084.63 87040.42
## 226 227 228 229 230 231 232 233
## 81042.08 80624.24 77276.02 76437.97 81172.42 82535.96 76974.43 72189.95
## 234 235 236 237 238 239 240 241
## 77840.54 64495.55 56536.35 48491.34 40107.44 44297.08 46448.66 39902.89
## 242 243 244 245 246 247 248 249
## 33585.87 43862.78 38112.78 42046.35 34314.41 33020.97 36674.28 39496.64
## 250 251 252 253 254 255 256 257
## 30331.07 36265.92 40065.28 45257.25 47974.09 47466.94 57762.31 75250.05
## 258 259 260 261 262 263 264 265
## 75065.20 68379.48 69860.51 66083.35 67528.71 61289.77 50570.20 46599.70
## 266 267 268 269 270 271 272 273
## 46809.00 42917.83 51717.20 47992.30 52136.79 50254.38 54319.55 54615.40
## 274 275 276 277 278 279 280 281
## 60629.58 58265.09 67932.83 61860.90 62070.82 60420.78 66160.56 59894.43
## 282 283 284 285 286 287 288 289
## 56460.14 46018.09 44415.70 61638.64 67165.75 67574.98 64993.97 64081.83
## 290 291 292 293 294 295 296 297
## 68080.63 71988.76 52995.55 43102.93 37117.19 47433.02 50673.10 49787.52
## 298 299 300 301 302 303 304 305
## 73970.83 79913.04 80580.34 85190.32 83389.85 78422.78 81887.74 56800.03
## 306 307 308 309 310 311 312 313
## 53063.15 52743.10 46536.59 43747.53 47348.95 39905.13 38627.91 33192.35
## 314 315 316 317 318 319 320 321
## 36974.87 36156.65 39986.08 37971.58 63744.18 61586.50 63209.22 71203.10
## 322 323 324 325 326 327 328 329
## 73589.16 99057.30 97435.82 73278.83 72077.32 70435.51 62391.50 59515.25
## 330 331 332 333 334 335 336 337
## 29479.54 33165.16 33604.45 35923.16 35264.34 41028.31 42103.35 37353.43
## 338 339 340 341 342 343 344 345
## 36568.26 36694.86 32016.28 38012.12 38675.18 38933.49 39808.84 41593.83
## 346 347 348 349 350 351 352 353
## 43414.88 43166.67 36114.99 26687.74 32029.62 30891.22 30481.49 28099.17
## 354 355 356 357 358 359 360 361
## 32775.63 36527.97 40989.27 39179.21 40438.85 42576.20 49967.63 50517.94
## 362 363 364 365 366 367 368 369
## 50719.50 53181.40 50666.11 50110.95 42759.92 39958.14 36137.07 33916.90
## 370 371 372 373 374 375 376 377
## 29976.71 37260.88 39546.66 47428.15 41402.87 40850.33 39388.38 38921.39
## 378 379 380 381 382 383 384 385
## 29793.73 34389.49 27444.72 35659.86 45987.66 49551.29 47825.54 49816.21
## 386 387 388 389 390 391 392 393
## 56033.20 65513.84 58728.59 53199.61 52928.45 60304.29 60827.40 69491.69
## 394 395 396 397 398 399 400 401
## 58602.86 60168.96 59729.29 59213.41 57701.07 56463.26 43228.53 51809.88
## 402 403 404 405 406 407 408 409
## 50810.80 49777.54 56175.64 48721.84 48033.34 46360.85 42040.24 40871.53
## 410 411 412 413 414 415 416 417
## 38936.36 33033.44 40902.34 43827.23 38502.18 33592.85 48457.17 52300.06
## 418 419 420 421 422 423 424 425
## 56227.40 48700.26 45025.37 43702.00 47286.71 35711.50 35440.83 29702.43
## 426 427 428 429 430 431 432 433
## 35289.54 43613.07 50501.85 47268.87 44338.99 41265.67 41153.92 37633.44
## 434 435 436 437 438 439 440 441
## 33773.71 31009.09 32584.42 34432.90 32438.49 37302.29 43504.81 40258.74
## 442 443 444 445 446 447 448 449
## 39964.88 42969.84 40856.94 44844.52 40093.72 31130.33 29968.36 41319.81
## 450 451 452 453 454 455 456 457
## 41001.50 46649.97 42288.20 42637.69 44257.42 47973.84 37852.97 42699.93
## 458 459 460 461 462 463 464 465
## 38125.37 45690.80 49209.16 51828.46 48559.57 50899.23 51100.67 52846.87
## 466 467 468 469 470 471 472 473
## 52344.71 55287.07 52616.62 57665.40 50928.54 48540.61 47127.43 43753.39
## 474 475 476 477 478 479 480 481
## 47507.73 54967.51 49397.12 51099.55 45890.87 44270.64 47102.24 36521.49
## 482 483 484 485 486 487 488 489
## 30086.10 31954.54 34649.93 36139.19 37104.59 30748.41 43272.68 49929.71
## 490 491 492 493 494 495 496 497
## 56756.75 51470.74 56301.49 63931.53 67741.12 54003.98 44586.33 42612.25
## 498 499 500 501 502 503 504 505
## 42935.29 43726.07 38215.43 40613.22 45912.20 51591.10 52300.44 52410.95
## 506 507 508 509 510 511 512 513
## 46115.88 47455.22 43716.22 46470.36 46136.21 39854.99 40985.53 40161.07
## 514 515 516 517 518 519 520 521
## 41266.17 43904.94 36748.49 32030.12 55908.34 64065.11 67716.30 61098.56
## 522 523 524 525 526 527 528 529
## 62437.40 76016.51 82989.85 57952.15 52824.67 49520.60 53897.46 53404.04
## 530 531 532 533 534 535 536 537
## 43592.37 48581.77 61236.14 55759.67 59155.66 63141.49 60195.52 55228.70
## 538 539 540 541 542 543 544 545
## 48694.15 47350.70 55283.95 55034.49 47598.16 49820.70 49647.45 50340.95
## 546 547 548 549 550 551 552 553
## 40994.51 32834.25 37173.94 45286.56 45083.62 46788.67 40801.43 49809.97
## 554 555 556 557 558 559 560 561
## 50964.71 40748.92 50285.19 58144.11 57509.36 61105.30 56874.62 68629.32
## 562 563 564 565 566 567 568 569
## 85309.68 75405.59 63974.81 68391.83 66517.16 67703.20 59277.39 43274.43
## 570 571 572 573 574 575 576 577
## 50279.58 56181.75 57363.93 59438.42 60085.14 57213.01 69453.52 58832.24
## 578 579 580 581 582 583 584 585
## 52558.38 60156.11 61659.34 54756.85 61020.23 56598.59 53644.64 67207.91
## 586 587 588 589 590 591 592 593
## 52643.81 59984.36 59077.20 52802.59 52108.10 52384.00 43104.55 45899.97
## 594 595 596 597 598 599 600 601
## 40529.15 44772.80 53531.63 46866.25 52722.27 55098.85 60765.04 56925.13
## 602 603 604 605 606 607 608 609
## 61736.42 53309.37 55188.41 55964.35 58271.33 58845.08 58386.08 52566.48
## 610 611 612 613 614 615 616 617
## 59624.51 57685.60 54784.29 51492.07 44458.61 55962.97 59848.40 50788.47
## 618 619 620 621 622 623 624 625
## 61185.12 65367.91 58861.05 81077.13 66207.58 58540.87 60542.77 55898.49
## 626 627 628 629 630 631 632 633
## 46238.86 56940.72 37507.09 37367.40 46930.73 57407.96 55453.58 84167.66
## 634 635 636 637 638 639 640 641
## 74358.99 76558.58 78195.02 72924.10 65647.17 62283.24 50194.51 48563.68
## 642 643 644 645 646 647 648 649
## 47455.84 45938.89 44324.40 46999.83 51614.80 66577.78 80994.44 78119.31
## 650 651 652 653 654 655 656 657
## 79022.60 84890.97 98328.51 93089.89 63375.74 60828.90 57784.89 58769.00
## 658 659 660 661 662 663 664 665
## 55211.86 45629.69 48012.76 52328.25 51520.88 63076.51 62954.28 63243.65
## 666 667 668 669 670 671 672 673
## 51705.23 53064.03 54081.94 49423.19 43406.14 46439.43 44039.52 47524.44
## 674 675 676 677 678 679 680 681
## 45278.08 38119.51 32780.37 32777.87 35525.40 40254.87 42514.08 40519.67
## 682 683 684 685 686 687 688 689
## 40543.24 40991.77 35337.18 41663.18 41160.90 41460.00 43454.41 54156.40
## 690 691 692 693 694 695 696 697
## 62609.15 70654.04 59956.67 55966.59 52850.61 58001.92 48300.01 42002.45
## 698 699 700 701 702 703 704 705
## 35901.46 32622.34 31103.14 36606.80 34871.32 35543.61 41473.35 70117.33
## 706 707 708 709 710 711 712 713
## 76272.58 93810.20 90131.45 92725.31 107742.29 106583.56 83957.75 84304.87
## 714 715 716 717 718 719 720 721
## 75647.93 72752.23 70729.87 53469.02 48869.89 52360.06 49866.23 38987.38
## 722 723 724 725 726 727 728 729
## 44552.28 40825.63 43069.50 40945.87 31658.31 35606.85 36231.74 29870.82
## 730 731 732 733 734 735 736 737
## 47928.44 50174.81 48209.83 53860.54 46271.42 46545.82 54522.73 40981.91
## 738 739 740 741 742 743 744 745
## 37395.96 45892.37 42668.75 44170.48 46937.59 46051.27 42472.42 49457.24
## 746 747 748 749 750 751 752 753
## 44481.18 65431.39 70748.58 66860.16 58800.18 78567.71 71646.01 70565.86
## 754 755 756 757 758 759 760 761
## 55811.68 104503.33 121563.87 126150.15 107686.91 110113.76 109361.65 107392.05
## 762 763 764 765 766 767 768 769
## 60097.74 45157.59 47065.69 45691.05 43668.82 152176.08 156610.32 181799.69
## 770 771 772 773 774 775 776 777
## 185364.06 179305.36 177303.96 184182.01 81461.05 72055.87 38450.67 41881.58
## 778 779 780 781 782 783 784 785
## 42290.32 46685.39 41087.56 41407.36 41250.08 45800.94 40541.37 40239.03
## 786 787 788 789 790 791 792 793
## 45349.92 48372.85 46628.39 43979.27 46716.32 50081.88 50487.50 45034.48
## 794 795 796 797 798 799 800 801
## 41072.84 41657.45 42067.31 36703.09 36784.79 36057.87 35948.61 47543.78
## 802 803 804 805 806 807 808 809
## 50271.35 56879.86 59027.06 53594.62 60752.44 68481.64 57358.82 50437.11
## 810 811 812 813 814 815 816 817
## 44293.09 48100.81 52466.33 50638.17 38654.72 36960.28 44533.57 52878.93
## 818 819 820 821 822 823 824 825
## 44535.07 38922.27 32632.19 43802.66 43981.02 41387.66 35563.19 44723.41
## 826 827 828 829 830 831 832 833
## 52763.18 57223.73 54069.09 53399.30 55960.48 59749.74 60403.20 53711.37
## 834 835 836 837 838 839 840 841
## 55531.54 54950.55 57194.42 60366.40 58530.77 49768.56 55217.97 50779.74
## 842 843 844 845 846 847 848 849
## 44529.83 48323.33 37611.49 37178.31 49413.84 51098.05 51979.63 51617.92
## 850 851 852 853 854
## 55819.41 66627.04 61773.72 50654.51 49979.23
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.809
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.27131 0.781609 3.986204
## t2* 2721.92923 163.693233 887.961302
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.195423 5.272817 13.7667
## 2 lag_depvar 1667.860082 2761.531150 4530.8501Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 25 00:59:15 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 25 00:59:25 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 25 00:59:35 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 25 00:59:45 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 25 00:59:55 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 25 01:00:05 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 25 01:00:14 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 25 01:00:24 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 25 01:00:34 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 25 01:00:44 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 8.205857 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 192.101143 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 54.522571 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 1.884286 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 26.407143 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 14.927000 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 15.711429 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 313.759429 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2808, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2808 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-09-09 00:04:58 sería de: 26.848 pesos// Percentil 95% más alto proyectado: 35.188,75
Según TimeGPT: La proyección de la UF a 298 días más 2026-07-04 sería de: 40.201,26 pesos// Percentil 80% más alto proyectado: 42.239,44 pesos// Percentil 95% más alto proyectado: 42.556,3
Según prophet: La proyección de la UF a 298 días más 2026-07-04 sería de: 42.517 pesos// Percentil 95% más alto proyectado: 50.093
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26460.95 | 26321.73 |
| Lo.80 | 26594.36 | 26486.94 |
| Point.Forecast | 26848.21 | 26799.01 |
| Hi.80 | 31608.29 | 32186.25 |
| Hi.95 | 34460.82 | 35038.08 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4788 1028.6175
## s.e. 0.1010 41.4439
##
## sigma^2 = 38077: log likelihood = -521.14
## AIC=1048.28 AICc=1048.61 BIC=1055.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4750 849.7566 5.4671
## s.e. 0.1014 348.7572 10.5782
##
## sigma^2 = 38456: log likelihood = -521.01
## AIC=1050.02 AICc=1050.57 BIC=1059.44forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 601.3384 | 592.9478 | 566.8658 |
| Lo.80 | 752.5218 | 743.7341 | 659.1232 |
| Point.Forecast | 1038.1140 | 1028.5762 | 876.3411 |
| Hi.80 | 1323.7061 | 1313.4183 | 1165.1444 |
| Hi.95 | 1474.8895 | 1464.2046 | 1354.7715 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.4.0 ggiraph_0.9.0
## [10] tidytext_0.4.3 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.11.0
## [16] xts_0.14.1 forecast_8.24.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.1.0
## [28] tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.9.0 lattice_0.22-6
## [34] GGally_2.3.0 ggplot2_3.5.2 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.9.1 httr_1.4.7
## [40] readxl_1.4.5 zoo_1.8-14 stringr_1.5.1
## [43] stringi_1.8.7 DataExplorer_0.8.4 data.table_1.17.8
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 httr2_1.2.1
## [4] lifecycle_1.0.4 StanHeaders_2.32.10 doParallel_1.0.17
## [7] globals_0.18.0 vroom_1.6.5 MASS_7.3-60.2
## [10] crosstalk_1.2.1 magrittr_2.0.3 sass_0.4.10
## [13] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
## [16] fracdiff_1.5-3 doRNG_1.8.6.2 askpass_1.2.1
## [19] pkgbuild_1.4.8 DBI_1.2.3 abind_1.4-8
## [22] quadprog_1.5-8 nnet_7.3-19 rappdirs_0.3.3
## [25] sandwich_3.1-1 inline_0.3.21 data.tree_1.1.0
## [28] tokenizers_0.3.0 listenv_0.9.1 anytime_0.3.12
## [31] spatial_7.3-17 parallelly_1.45.1 codetools_0.2-20
## [34] xml2_1.3.8 tidyselect_1.2.1 farver_2.1.2
## [37] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [40] stats4_4.4.0 jsonlite_2.0.0 ellipsis_0.3.2
## [43] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.3
## [46] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [49] xfun_0.52 TTR_0.24.4 ggfortify_0.4.19
## [52] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [55] fastmap_1.2.0 boot_1.3-30 openssl_2.3.3
## [58] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [61] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [64] networkD3_0.4.1 gtools_3.9.5 generics_0.1.4
## [67] htmlwidgets_1.6.4 ggstats_0.10.0 pkgconfig_2.0.3
## [70] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [73] S7_0.2.0 selectr_0.4-2 janeaustenr_1.0.0
## [76] htmltools_0.5.8.1 carData_3.0-5 tseries_0.10-58
## [79] snakecase_0.11.1 knitr_1.50 rstudioapi_0.17.1
## [82] tzdb_0.5.0 uuid_1.2-1 nlme_3.1-164
## [85] curl_6.4.0 cachem_1.1.0 KernSmooth_2.23-22
## [88] parallel_4.4.0 fBasics_4041.97 pillar_1.11.0
## [91] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [94] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [97] evaluate_1.0.4 mvtnorm_1.3-3 cli_3.6.5
## [100] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [103] future.apply_1.20.0 labeling_0.4.3 rstan_2.32.7
## [106] QuickJSR_1.8.0 viridisLite_0.4.2 assertthat_0.2.1
## [109] lazyeval_0.2.2 Matrix_1.7-0 hms_1.1.3
## [112] bit64_4.6.0-1 future_1.67.0 nixtlar_0.6.2
## [115] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [118] bslib_0.9.0 quantmod_0.4.28 bit_4.6.0#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))